{"id":282682,"date":"2021-06-24T14:19:16","date_gmt":"2021-06-24T11:19:16","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/designing-temporal-networks-that-synchronize-under-resource-constraints\/"},"modified":"2021-06-24T14:19:16","modified_gmt":"2021-06-24T11:19:16","slug":"designing-temporal-networks-that-synchronize-under-resource-constraints","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/designing-temporal-networks-that-synchronize-under-resource-constraints\/","title":{"rendered":"#Designing temporal networks that synchronize under resource constraints"},"content":{"rendered":"<p>&#8220;<strong>#Designing temporal networks that synchronize under resource constraints<\/strong>&#8221;<\/p>\n<div>\n<div class=\"article-gallery lightGallery\">\n<div data-thumb=\"https:\/\/scx1.b-cdn.net\/csz\/news\/tmb\/2021\/designing-temporal-net.jpg\" data-src=\"https:\/\/scx2.b-cdn.net\/gfx\/news\/hires\/2021\/designing-temporal-net.jpg\" data-sub-html=\"Designing temporal networks that synchronize better than optimal static networks. a Evolution of the nonzero Laplacian eigenvalues described in Eq. (5), which are split into two degenerate groups. b Temporal network constructed from the Laplacian eigenvalues in a. The weight of each edge is represented by its thickness. In addition, edges whose weight is larger than 1n1n are colored orange, whereas those with weight less than 1n1n are colored cyan. For this network diagram, we set n\u2009=\u200911 and m\u2009=\u20095, and the corresponding weighted adjacency matrix is given by Eq. (9). Visually, we can see that different parts of the network are being strengthened in an alternating fashion. From: Designing temporal networks that synchronize under resource constraints\">\n<figure class=\"article-img\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/scx1.b-cdn.net\/csz\/news\/800a\/2021\/designing-temporal-net.jpg\" alt=\"Designing temporal networks that synchronize under resource constraints\" title=\"Designing temporal networks that synchronize better than optimal static networks. a Evolution of the nonzero Laplacian eigenvalues described in Eq. (5), which are split into two degenerate groups. b Temporal network constructed from the Laplacian eigenvalues in a. The weight of each edge is represented by its thickness. In addition, edges whose weight is larger than 1n1n are colored orange, whereas those with weight less than 1n1n are colored cyan. For this network diagram, we set n\u2009=\u200911 and m\u2009=\u20095, and the corresponding weighted adjacency matrix is given by Eq. (9). Visually, we can see that different parts of the network are being strengthened in an alternating fashion. From: Designing temporal networks that synchronize under resource constraints\" width=\"800\" height=\"246\"\/><figcaption class=\"text-darken text-low-up text-truncate-js text-truncate mt-3\">\n                Designing temporal networks that synchronize better than optimal static networks. a Evolution of the nonzero Laplacian eigenvalues described in Eq. (5), which are split into two degenerate groups. b Temporal network constructed from the Laplacian eigenvalues in a. The weight of each edge is represented by its thickness. In addition, edges whose weight is larger than 1n1n are colored orange, whereas those with weight less than 1n1n are colored cyan. For this network diagram, we set n\u2009=\u200911 and m\u2009=\u20095, and the corresponding weighted adjacency matrix is given by Eq. (9). Visually, we can see that different parts of the network are being strengthened in an alternating fashion. From: Designing temporal networks that synchronize under resource constraints<br \/>\n            <\/figcaption><\/figure>\n<\/div>\n<\/div>\n<p>Synchronization is critical for the function of many distributed systems\u2014whether it&#8217;s computers or power grids or neuronal populations\u2014but doing it using the least amount of energy and resources possible can be a daunting task.<\/p>\n<p>                                                                                In a paper published in <i>Nature Communications<\/i> in June 2021, incoming SFI Postdoctoral Fellow Yuanzhao Zhang and former SFI external faculty member Steve Strogatz report using temporal network models to show that allowing connection patterns to change over time makes it possible to synchronize a system more efficiently.<\/p>\n<p>&#8220;This was a fun project started by accident,&#8221; says Zhang. &#8220;I was researching circadian clocks and came across an interesting paper about the energy cost of synchronizing them. It piqued my curiosity, so I wanted to figure out the best way to synchronize a generic networked system using the least amount of resources.&#8221;<\/p>\n<p>The researchers&#8217; temporal network design is &#8220;open loop,&#8221; so it&#8217;s versatile and expected to work for a wide range of systems.\n                                                                                                                        <\/p>\n<hr\/>\n<div class=\"article-main__explore my-4 d-print-none\">\n<p>                                            Exploring partial synchronization in networked systems\n                                        <\/p><\/div>\n<hr class=\"mb-4\"\/>\n<div class=\"article-main__more p-4\">\n                                                                                                <strong>More information:<\/strong><br \/>\n                                                Yuanzhao Zhang et al, Designing temporal networks that synchronize under resource constraints, <i>Nature Communications<\/i> (2021).  <a rel=\"nofollow noopener\" target=\"_blank\" data-doi=\"1\" href=\"http:\/\/dx.doi.org\/10.1038\/s41467-021-23446-9\">DOI: 10.1038\/s41467-021-23446-9<\/a><\/p><\/div>\n<div class=\"d-inline-block text-medium my-4\">\n                                                Provided by<br \/>\n                                                                                                    Santa Fe Institute<br \/>\n                                                                                                        <a rel=\"nofollow noopener\" target=\"_blank\" class=\"icon_open\" href=\"http:\/\/www.santafe.edu\"><br \/>\n                                                        <svg><use href=\"https:\/\/techx.b-cdn.net\/tmpl\/v2\/img\/svg\/sprite.svg#icon_open\" x=\"0\" y=\"0\"\/><\/svg><\/a><\/p><\/div>\n<p>                                        <!-- print only --><\/p>\n<div class=\"d-none d-print-block\">\n<p>                                                 <strong>Citation<\/strong>:<br \/>\n                                                 Designing temporal networks that synchronize under resource constraints (2021, June 24)<br \/>\n                                                 retrieved 24 June 2021<br \/>\n                                                 from https:\/\/techxplore.com\/<a href=\"https:\/\/buradabiliyorum.com\/en\/category\/news\/\" data-internallinksmanager029f6b8e52c=\"2\" title=\"News\" target=\"_blank\" rel=\"noopener\">news<\/a>\/2021-06-temporal-networks-synchronize-resource-constraints.html<\/p>\n<p>                                            This document is subject to copyright. 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(5), which are split into two degenerate groups. b Temporal network constructed from the Laplacian eigenvalues in a. 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